---
phase: 03-training-energy-tests
verified: 2026-02-24T21:02:05Z
status: passed
score: 11/11 must-haves verified
gaps: []
---

# Phase 3: Training + Energy + Tests Verification Report

**Phase Goal:** A PredictiveCodingNetwork can be trained end-to-end on batched data using local Hebbian-like rules, with energy tracking and verified algorithmic correctness
**Verified:** 2026-02-24T21:02:05Z
**Status:** passed
**Re-verification:** No -- initial verification

## Goal Achievement

### Observable Truths

| # | Truth | Status | Evidence |
|---|-------|--------|----------|
| 1 | train_pcn trains network and returns TrainHistory with energy and accuracy | VERIFIED | smoke test: per_batch=2, per_epoch=1, train_accuracy=[0.375]; test_train_pcn_classification PASSED |
| 2 | All training ops under torch.no_grad() with no autograd graph | VERIFIED | 6 torch.no_grad() blocks in trainer.py; test_no_autograd_graph_during_training PASSED (0 RuntimeWarnings, all latents grad_fn=None) |
| 3 | Inference loop updates latents for T_infer steps using snapshot errors | VERIFIED | compute_errors() at line 350 precedes latent update loop at line 368; model.latents[idx] -= confirmed 5 occurrences |
| 4 | Learning loop updates weights for T_learn steps with batch-averaged .data mutation | VERIFIED | weights[idx].data -= and weights[-1].data -= in trainer.py; divided by B; test_train_pcn_weights_change PASSED |
| 5 | Classification one-hot encoded; regression targets pass through unchanged | VERIFIED | F.one_hot at trainer.py:308; else branch at line 315 passes y_batch unchanged; test_train_pcn_regression PASSED |
| 6 | Energy tracked per-step (rolling window), per-batch, and per-epoch | VERIFIED | deque at line 339; history.energy.per_batch.append line 415; history.energy.per_epoch.append line 434 |
| 7 | test_pcn returns accuracy and energy dict | VERIFIED | trainer.py:450-566 returns dict with float accuracy/energy; test_test_pcn_returns_metrics PASSED |
| 8 | RichCallback provides console display; TrainCallback enables custom hooks | VERIFIED | RichCallback at trainer.py:167 using rich.console.Console; TrainCallback at 33-58 with 8 no-op hook methods |
| 9 | TST-02: gain-modulated error uses preactivation f-prime(a^l) | VERIFIED | test_gain_modulated_error_uses_preactivation PASSED: manual preact matches errors.gm_errors[0] |
| 10 | TST-03: no autograd graph built during training | VERIFIED | test_no_autograd_graph_during_training PASSED: 0 RuntimeWarnings; all latents grad_fn=None |
| 11 | TST-04: top_error = supervised_error @ W_out, shape (B, d_L) | VERIFIED | test_top_error_wout_projection PASSED: manual expected_top matches; shape (4, 5) confirmed |

**Score:** 11/11 truths verified

### Required Artifacts

| Artifact | Expected | Status | Details |
|----------|----------|--------|---------|
| src/pcn_torch/energy.py | compute_energy, compute_energy_per_layer | VERIFIED | 63 lines, no stubs, both functions exported and imported by trainer.py |
| src/pcn_torch/trainer.py | TrainConfig, TrainHistory, EnergyHistory, TrainCallback, RichCallback, train_pcn, test_pcn | VERIFIED | 566 lines, no stubs, all 7 names exported |
| src/pcn_torch/__init__.py | Re-exports all 9 Phase 3 public names | VERIFIED | 41 lines, all 9 names in __all__ |
| tests/test_energy.py | 8 energy computation tests | VERIFIED | 127 lines, 8/8 PASSED |
| tests/test_trainer.py | 14 training tests (TST-02, TST-03, TST-04) | VERIFIED | 304 lines, 14/14 PASSED |
| pyproject.toml | rich>=14.0 dependency | VERIFIED | Present in [project] dependencies |

### Key Link Verification

| From | To | Via | Status |
|------|----|-----|--------|
| trainer.py | energy.py | from pcn_torch.energy import compute_energy (line 20) | WIRED |
| trainer.py | network.py | TYPE_CHECKING PredictiveCodingNetwork; model.compute_errors(), init_latents(), latents, predict() | WIRED |
| train_pcn inference | model.latents | model.latents[idx] -= lr_infer * grad_x | WIRED |
| train_pcn learning | layer.weight.data | weights[idx].data -= lr_learn * grad_W | WIRED |
| tests/test_energy.py | energy.py | from pcn_torch.energy import compute_energy, compute_energy_per_layer | WIRED |
| tests/test_trainer.py | trainer.py | from pcn_torch.trainer import TrainCallback, TrainConfig, TrainHistory, train_pcn | WIRED |
| __init__.py | trainer.py | from pcn_torch.trainer import (7 names re-exported) | WIRED |
| __init__.py | energy.py | from pcn_torch.energy import compute_energy, compute_energy_per_layer | WIRED |

### Requirements Coverage

| Requirement | Status | Verified By |
|-------------|--------|-------------|
| TRN-01: train_pcn two-phase (inference + learning) loop | SATISFIED | trainer.py:331-441; smoke test execution confirms |
| TRN-02: Inference loop updates latents for T_infer steps | SATISFIED | for t in range(config.T_infer) at line 343; model.latents[idx] -= at line 373 |
| TRN-03: Learning loop weight updates, T_learn steps, 1/B averaged | SATISFIED | for t in range(T_learn) at line 388; weights[idx].data -= / B at line 401 |
| TRN-04: Classification and regression both supported | SATISFIED | F.one_hot at line 308; else branch at 315; test_train_pcn_regression PASSED |
| TRN-05: test_pcn inference-only evaluation | SATISFIED | trainer.py:450-566; no weight update loop in test_pcn body |
| NRG-01: Per-step energy (rolling window) | SATISFIED | deque(maxlen=config.energy_window_size) at line 339 |
| NRG-02: Per-batch and per-epoch energy accessible after training | SATISFIED | per_batch.append line 415; per_epoch.append line 434 |
| TST-01: Synchronous latent updates (all errors before any update) | SATISFIED | compute_errors() at line 350 before latent update loop at line 368 |
| TST-02: Gain-modulated error uses preactivation | SATISFIED | test_gain_modulated_error_uses_preactivation PASSED |
| TST-03: No autograd graph during training | SATISFIED | test_no_autograd_graph_during_training PASSED |
| TST-04: Correct W_out^T supervised error projection | SATISFIED | test_top_error_wout_projection PASSED |

### Anti-Patterns Found

No anti-patterns detected. All 5 key files are clean: no TODO/FIXME, no placeholder text, no empty returns, no stub patterns.

### Human Verification Required

None. All success criteria are structurally verifiable from source code and test output.

### Gaps Summary

No gaps. All 11 must-have truths verified. All 6 required artifacts exist, are substantive, and are correctly wired. All 11 requirements (TRN-01 through TST-04) satisfied by concrete code and passing tests.

Full test results:
- tests/test_energy.py: 8/8 passed
- tests/test_trainer.py: 14/14 passed (includes TST-02, TST-03, TST-04)
- Full suite (Phases 1-3): 101/101 passed in 2.87s, zero regressions

---

_Verified: 2026-02-24T21:02:05Z_
_Verifier: Claude (gsd-verifier)_
